Abstract
In the process of machining mechanical products, high productivity machining and small roughness of the product surface are desirable in all cases. In this study, a new approach to improve milling productivity will be presented on the basis of ensuring the minimum required surface roughness. The experiments have been conducted for 40CrMn steel using TiAlN coated cutting tools. The experimental matrix of 15 experiments has been designed using Box-Behnken method. A regression model showing the relationship between surface roughness and cutting velocity, feed rate and cutting depth has been set up. Sub-regression models representing the relationship between surface roughness and each input parameter have also been created. From these models, the effect of cutting parameters on surface roughness has been determined. The main objective of this study is to determine the value of cutting parameters to improve the cutting productivity while ensuring the minimum surface roughness value.
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This work was supported by Thai Nguyen University of Technology.
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Linh, N.H., Tuan, T.K., Quang, N.H., Lam, P.D., Anh, L.H., Thao, T.T.P. (2022). A New Approach for Milling Productivity Improvement. In: Nguyen, D.C., Vu, N.P., Long, B.T., Puta, H., Sattler, KU. (eds) Advances in Engineering Research and Application. ICERA 2021. Lecture Notes in Networks and Systems, vol 366. Springer, Cham. https://doi.org/10.1007/978-3-030-92574-1_7
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